In [12]:
import pandas as pd

import seaborn as sns
import matplotlib.pyplot as plt
In [11]:
p=pd.read_csv("G:/My Drive/INNO_INTERN/DATASETS/project2.csv")
In [3]:
p.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 112634 entries, 0 to 112633
Data columns (total 17 columns):
 #   Column                                             Non-Null Count   Dtype  
---  ------                                             --------------   -----  
 0   VIN (1-10)                                         112634 non-null  object 
 1   County                                             112634 non-null  object 
 2   City                                               112634 non-null  object 
 3   State                                              112634 non-null  object 
 4   Postal Code                                        112634 non-null  int64  
 5   Model Year                                         112634 non-null  int64  
 6   Make                                               112634 non-null  object 
 7   Model                                              112614 non-null  object 
 8   Electric Vehicle Type                              112634 non-null  object 
 9   Clean Alternative Fuel Vehicle (CAFV) Eligibility  112634 non-null  object 
 10  Electric Range                                     112634 non-null  int64  
 11  Base MSRP                                          112634 non-null  int64  
 12  Legislative District                               112348 non-null  float64
 13  DOL Vehicle ID                                     112634 non-null  int64  
 14  Vehicle Location                                   112610 non-null  object 
 15  Electric Utility                                   112191 non-null  object 
 16  2020 Census Tract                                  112634 non-null  int64  
dtypes: float64(1), int64(6), object(10)
memory usage: 14.6+ MB
In [4]:
p.head()
Out[4]:
VIN (1-10) County City State Postal Code Model Year Make Model Electric Vehicle Type Clean Alternative Fuel Vehicle (CAFV) Eligibility Electric Range Base MSRP Legislative District DOL Vehicle ID Vehicle Location Electric Utility 2020 Census Tract
0 JTMEB3FV6N Monroe Key West FL 33040 2022 TOYOTA RAV4 PRIME Plug-in Hybrid Electric Vehicle (PHEV) Clean Alternative Fuel Vehicle Eligible 42 0 NaN 198968248 POINT (-81.80023 24.5545) NaN 12087972100
1 1G1RD6E45D Clark Laughlin NV 89029 2013 CHEVROLET VOLT Plug-in Hybrid Electric Vehicle (PHEV) Clean Alternative Fuel Vehicle Eligible 38 0 NaN 5204412 POINT (-114.57245 35.16815) NaN 32003005702
2 JN1AZ0CP8B Yakima Yakima WA 98901 2011 NISSAN LEAF Battery Electric Vehicle (BEV) Clean Alternative Fuel Vehicle Eligible 73 0 15.0 218972519 POINT (-120.50721 46.60448) PACIFICORP 53077001602
3 1G1FW6S08H Skagit Concrete WA 98237 2017 CHEVROLET BOLT EV Battery Electric Vehicle (BEV) Clean Alternative Fuel Vehicle Eligible 238 0 39.0 186750406 POINT (-121.7515 48.53892) PUGET SOUND ENERGY INC 53057951101
4 3FA6P0SU1K Snohomish Everett WA 98201 2019 FORD FUSION Plug-in Hybrid Electric Vehicle (PHEV) Not eligible due to low battery range 26 0 38.0 2006714 POINT (-122.20596 47.97659) PUGET SOUND ENERGY INC 53061041500
In [5]:
p.tail()
Out[5]:
VIN (1-10) County City State Postal Code Model Year Make Model Electric Vehicle Type Clean Alternative Fuel Vehicle (CAFV) Eligibility Electric Range Base MSRP Legislative District DOL Vehicle ID Vehicle Location Electric Utility 2020 Census Tract
112629 7SAYGDEF2N King Duvall WA 98019 2022 TESLA MODEL Y Battery Electric Vehicle (BEV) Eligibility unknown as battery range has not b... 0 0 45.0 217955265 POINT (-121.98609 47.74068) PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA) 53033032401
112630 1N4BZ1CP7K San Juan Friday Harbor WA 98250 2019 NISSAN LEAF Battery Electric Vehicle (BEV) Clean Alternative Fuel Vehicle Eligible 150 0 40.0 103663227 POINT (-123.01648 48.53448) BONNEVILLE POWER ADMINISTRATION||ORCAS POWER &... 53055960301
112631 1FMCU0KZ4N King Vashon WA 98070 2022 FORD ESCAPE Plug-in Hybrid Electric Vehicle (PHEV) Clean Alternative Fuel Vehicle Eligible 38 0 34.0 193878387 POINT (-122.4573 47.44929) PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA) 53033027702
112632 KNDCD3LD4J King Covington WA 98042 2018 KIA NIRO Plug-in Hybrid Electric Vehicle (PHEV) Not eligible due to low battery range 26 0 47.0 125039043 POINT (-122.09124 47.33778) PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA) 53033032007
112633 YV4BR0CL8N King Covington WA 98042 2022 VOLVO XC90 Plug-in Hybrid Electric Vehicle (PHEV) Not eligible due to low battery range 18 0 47.0 194673692 POINT (-122.09124 47.33778) PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA) 53033032005
In [7]:
p.describe()
Out[7]:
Postal Code Model Year Electric Range Base MSRP Legislative District DOL Vehicle ID 2020 Census Tract
count 112634.000000 112634.000000 112634.000000 112634.000000 112348.000000 1.126340e+05 1.126340e+05
mean 98156.226850 2019.003365 87.812987 1793.439681 29.805604 1.994567e+08 5.296650e+10
std 2648.733064 2.892364 102.334216 10783.753486 14.700545 9.398427e+07 1.699104e+09
min 1730.000000 1997.000000 0.000000 0.000000 1.000000 4.777000e+03 1.101001e+09
25% 98052.000000 2017.000000 0.000000 0.000000 18.000000 1.484142e+08 5.303301e+10
50% 98119.000000 2020.000000 32.000000 0.000000 34.000000 1.923896e+08 5.303303e+10
75% 98370.000000 2022.000000 208.000000 0.000000 43.000000 2.191899e+08 5.305307e+10
max 99701.000000 2023.000000 337.000000 845000.000000 49.000000 4.792548e+08 5.603300e+10
In [14]:
# ------------- Univariate Analysis -------------
# 1. Distribution of Model Year
plt.figure(figsize=(10, 6))
sns.histplot(p['Model Year'], kde=True, bins=30, color='green')
plt.title('Distribution of Model Year')
plt.xlabel('Model Year')
plt.ylabel('Count')
plt.show()

C:\Users\sadgu\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
In [17]:
# 2. Distribution of Electric Vehicle Type
plt.figure(figsize=(10, 6))
sns.countplot(data=p, x='Electric Vehicle Type', palette='Set3')
plt.title('Count of Electric Vehicle Types')
plt.xlabel('Electric Vehicle Type')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.show()
In [18]:
# 3. Distribution of Electric Range
plt.figure(figsize=(10, 6))
sns.histplot(p['Electric Range'], kde=True, bins=30, color='red')
plt.title('Distribution of Electric Range')
plt.xlabel('Electric Range (miles)')
plt.ylabel('Count')
plt.show()

C:\Users\sadgu\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
In [19]:
# ------------- Bivariate Analysis -------------
# 4. Electric Range vs. Model Year
plt.figure(figsize=(10, 6))
sns.scatterplot(data=p, x='Model Year', y='Electric Range', hue='Electric Vehicle Type', palette='coolwarm')
plt.title('Electric Range vs. Model Year')
plt.xlabel('Model Year')
plt.ylabel('Electric Range (miles)')
plt.legend(title='Electric Vehicle Type')
plt.show()
In [22]:
# 5. Electric Range by Make
plt.figure(figsize=(10, 6))
sns.boxplot(data=p, x='Make', y='Electric Range', palette='Set1')
plt.title('Electric Range by Vehicle Make')
plt.xlabel('Make')
plt.ylabel('Electric Range (miles)')
plt.xticks(rotation=90)
plt.show()
In [23]:
# 6. Base MSRP vs Electric Range
plt.figure(figsize=(10, 6))
sns.scatterplot(data=p, x='Base MSRP', y='Electric Range', hue='Electric Vehicle Type', palette='viridis')
plt.title('Base MSRP vs Electric Range')
plt.xlabel('Base MSRP (USD)')
plt.ylabel('Electric Range (miles)')
plt.legend(title='Electric Vehicle Type')
plt.show()
In [24]:
# 7. Count of Clean Alternative Fuel Vehicle Eligibility
plt.figure(figsize=(10, 6))
sns.countplot(data=p, x='Clean Alternative Fuel Vehicle (CAFV) Eligibility', palette='Set1')
plt.title('Count of CAFV Eligibility')
plt.xlabel('CAFV Eligibility')
plt.ylabel('Count')
plt.xticks(rotation=45)
plt.show()
In [26]:
# Missing Data Check
print(p.isnull().sum())

VIN (1-10)                                             0
County                                                 0
City                                                   0
State                                                  0
Postal Code                                            0
Model Year                                             0
Make                                                   0
Model                                                 20
Electric Vehicle Type                                  0
Clean Alternative Fuel Vehicle (CAFV) Eligibility      0
Electric Range                                         0
Base MSRP                                              0
Legislative District                                 286
DOL Vehicle ID                                         0
Vehicle Location                                      24
Electric Utility                                     443
2020 Census Tract                                      0
dtype: int64
In [27]:
import seaborn as sns
import matplotlib.pyplot as plt

# Heatmap to visualize missing data
plt.figure(figsize=(10, 6))
sns.heatmap(p.isnull(), cbar=False, cmap='viridis')
plt.title('Missing Data Heatmap')
plt.show()
In [29]:
# Boxplot for outlier detection in Electric Range
plt.figure(figsize=(10, 6))
sns.boxplot(data=p, y='Electric Range')
plt.title('Boxplot of Electric Range (Outlier Detection)')
plt.show()

# Boxplot for outlier detection in Base MSRP
plt.figure(figsize=(10, 6))
sns.boxplot(data=p, y='Base MSRP')
plt.title('Boxplot of Base MSRP (Outlier Detection)')
plt.show()
In [31]:
# Correlation matrix and heatmap
plt.figure(figsize=(10, 6))
corr_matrix = p[['Electric Range', 'Model Year', 'Base MSRP']].corr()
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title('Correlation Heatmap')
plt.show()
In [32]:
# Boxplot for Electric Range by CAFV Eligibility
plt.figure(figsize=(10, 6))
sns.boxplot(data=p, x='Clean Alternative Fuel Vehicle (CAFV) Eligibility', y='Electric Range')
plt.title('Electric Range by CAFV Eligibility')
plt.xlabel('CAFV Eligibility')
plt.ylabel('Electric Range (miles)')
plt.xticks(rotation=45)
plt.show()
In [34]:
# Electric Range by City (Top 10 Cities)
top_cities = p['City'].value_counts().nlargest(10).index
filtered_data = p[p['City'].isin(top_cities)]

plt.figure(figsize=(10, 6))
sns.boxplot(data=filtered_data, x='City', y='Electric Range', palette='Set3')
plt.title('Electric Range by City (Top 10 Cities)')
plt.xlabel('City')
plt.ylabel('Electric Range (miles)')
plt.xticks(rotation=90)
plt.show()

Task 2: Create a Choropleth using plotly.express to display the number of EV vehicles based on location.¶

In [35]:
!pip install plotly

Requirement already satisfied: plotly in c:\users\sadgu\anaconda3\lib\site-packages (5.9.0)
Requirement already satisfied: tenacity>=6.2.0 in c:\users\sadgu\anaconda3\lib\site-packages (from plotly) (8.2.2)
In [36]:
import plotly.express as px
In [37]:
scatter_plot = px.scatter(p, x="Electric Range", y="Base MSRP", title="Scatter Plot: Electric Range vs Base MSRP")
scatter_plot.show()
In [39]:
box_plot = px.box(p, x="Electric Vehicle Type", y="Electric Range", title="Box Plot: Electric Vehicle Type vs Electric Range")
box_plot.show()
In [40]:
vehicle_type_count = p['Electric Vehicle Type'].value_counts().reset_index()
vehicle_type_count.columns = ['Electric Vehicle Type', 'Count']
pie_chart = px.pie(vehicle_type_count, names='Electric Vehicle Type', values='Count', title="Pie Chart: Distribution of Electric Vehicle Types")
pie_chart.show()
In [41]:
vehicle_count_by_state = p['State'].value_counts().reset_index()
vehicle_count_by_state.columns = ['State', 'Vehicle Count']

choropleth = px.choropleth(vehicle_count_by_state,
                           locations="State",
                           locationmode="USA-states",
                           color="Vehicle Count",
                           scope="usa",
                           title="Choropleth Map: Number of EV Vehicles by State")
choropleth.show()
In [43]:
animated_choropleth = px.choropleth(p,
                                    locations="State",
                                    locationmode="USA-states",
                                    color="Electric Range",
                                    animation_frame="Model Year",
                                    scope="usa",
                                    title="Animated Choropleth: Electric Range over Model Year by State")
animated_choropleth.show()

C:\Users\sadgu\anaconda3\Lib\site-packages\plotly\express\_core.py:1979: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

Task 3: Create a Racing Bar Plot to display the animation of EV Make and its count each year.¶

In [44]:
!pip install bar_chart_race

Collecting bar_chart_race
  Obtaining dependency information for bar_chart_race from https://files.pythonhosted.org/packages/09/01/f6d1a1a0978b39560843c54be7349804d7d2faef0a869acd7c8a6fc920b0/bar_chart_race-0.1.0-py3-none-any.whl.metadata
  Downloading bar_chart_race-0.1.0-py3-none-any.whl.metadata (4.2 kB)
Requirement already satisfied: pandas>=0.24 in c:\users\sadgu\anaconda3\lib\site-packages (from bar_chart_race) (2.2.3)
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Installing collected packages: bar_chart_race
Successfully installed bar_chart_race-0.1.0
In [47]:
# Assuming you have already loaded your dataset
# Create a pivot table with counts of vehicles by 'Make' and 'Model Year'
pivot_data = p.pivot_table(index="Model Year", columns="Make", aggfunc="size", fill_value=0)

# Sort the columns by sum of vehicle counts
pivot_data = pivot_data.loc[:, pivot_data.sum(axis=0).sort_values(ascending=False).index]
In [46]:
import bar_chart_race as bcr
In [49]:
# Create a pivot table with counts of vehicles by 'Make' and 'Model Year'
pivot_data = p.pivot_table(index="Model Year", columns="Make", aggfunc="size", fill_value=0)

# Reset index to make 'Model Year' a column
pivot_data.reset_index(inplace=True)
melted_data = pivot_data.melt(id_vars=["Model Year"], var_name="Make", value_name="Count")

# Create an animated bar plot
fig = px.bar(melted_data,
             x='Count',
             y='Make',
             color='Make',
             animation_frame='Model Year',
             range_x=[0, melted_data['Count'].max() + 10],  # Adjust range for better visualization
             title='Year-wise EV Make Sales Animation',
             orientation='h')


fig.update_layout(
    title_font=dict(size=30), 
    xaxis_title_font=dict(size=20), 
    yaxis_title_font=dict(size=20),  
    width=1000,  
    height=600,  
    bargap=0.1,  
)